A salient region detector for GPU using a cellular automata architecture

Authors:
David Huw Jones;Adam Powell;Christos-Savvas Bouganis;Peter Y. K. Cheung
Affiliations:
Imperial college London, Electrical and Electronic Engineering, London;Imperial college London, Electrical and Electronic Engineering, London;Imperial college London, Electrical and Electronic Engineering, London;Imperial college London, Electrical and Electronic Engineering, London
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

Citing 3
Cited 0

A Model of Saliency-Based Visual Attention for Rapid Scene Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
HereBoy: A Fast Evolutionary Algorithm

EH '00 Proceedings of the 2nd NASA/DoD workshop on Evolvable Hardware
A Comparison of Affine Region Detectors

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

The human visual cortex performs salient region detection, a process critical to the rapid understanding of a scene. This is performed on large arrays of locally interacting neurons that are slow to simulate sequentially. In this paper we describe and evaluate a novel, bio-inspired, cellular automata (CA) architecture for the determination of the salient regions within a scene. This parallel processing architecture is appropriate for implementation on a graphics processing unit (GPU). We compare the performance of this algorithm against that of CPU implemented salient region detectors. The CA algorithm is less subject to variation due to changing scale, viewpoint and illumination conditions. Also due to its GPU implementation, this algorithm is able to detect salient regions faster than the CPU implemented algorithms.